1. Data
Warehousing

2. Outline What is a data warehouse? A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
Further development of data cube technology
From data warehousing to data mining

3. What is Data Warehouse?
Defined in many different ways, but not rigorously
A decision support database that is maintained separately from the organisation’s operational database
Support information processing by providing a solid platform of consolidated, historical data for analysis
Definition by Inmon
“A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’s decision-making process”
Data warehousing
The process of constructing and using data warehouses

4. Data Warehouse—Subject-Oriented
Organized around major subjects, such as customer, product, sales
Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing
Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process

5. Data Warehouse—Integrated
Constructed by integrating multiple, heterogeneous data sources
relational databases, flat files, on-line transaction records
Data cleaning and data integration techniques are applied
Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources
E.g., Hotel price: currency, tax, breakfast covered, etc.
When data is moved to the warehouse, it is converted

6. Data Warehouse—Time Variant
The time horizon for the data warehouse is significantly longer than that of operational systems
Operational database: current value data
Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)
Every key structure in the data warehouse
Contains an element of time, explicitly or implicitly
But the key of operational data may or may not contain “time element”

7. Data Warehouse—Non-Volatile
A physically separate store of data transformed from the operational environment
Operational update of data does not occur in the data warehouse environment
Does not require transaction processing, recovery, and concurrency control mechanisms
Requires only two operations in data accessing:
initial loading of data and access of data

8. Data Warehouse vs. Heterogeneous DBMS
Traditional heterogeneous DB integration
Build wrappers/mediators on top of heterogeneous databases
Query driven approach
When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set
Complex information filtering, compete for resources
Data warehouse
update-driven, high performance
Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis

9. Data Warehouse vs. Operational DBMS
OLTP (On-Line Transaction Processing)
Major task of traditional relational DBMS
Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
OLAP (On-Line Analytical Processing)
Major task of data warehouse system
Data analysis and decision making
Distinct features (OLTP vs. OLAP):
User and system orientation: customer vs. market
Data contents: current, detailed vs. historical, consolidated
Database design: ER + application vs. star + subject
View: current, local vs. evolutionary, integrated
Access patterns: update vs. read-only but complex queries

10. OLTP vs. OLAP

11. Why Separate Data Warehouse?
High performance for both systems
DBMS— tuned for OLTP
access methods, indexing, concurrency control, recovery
Warehouse—tuned for OLAP
complex OLAP queries, multidimensional view, consolidation.
Different functions and different data
Missing data: Decision support requires historical data which operational DBs do not typically maintain
Data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources
Data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

12. Outline What is a data warehouse? A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
Further development of data cube technology
From data warehousing to data mining

13. From Tables and Spreadsheets to Data Cubes
A data warehouse is based on
multidimensional data model which views data in the form of a data cube
A data cube allows data to be modeled and viewed in multiple dimensions (such as sales)
Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)
Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables
Definitions
an n-Dimensional base cube is called a base cuboid
The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid
The lattice of cuboids forms a data cube

14. Cube: A Lattice of Cuboids
all
0-D(apex) cuboid
time
item
location
supplier
1-D cuboids
time,item
time,location
item,location
location,supplier
2-D cuboids
time,supplier
item,supplier
time,location,supplier
time,item,location
3-D cuboids
time,item,supplier
item,location,supplier
4-D(base) cuboid
time, item, location, supplier

15. Conceptual Modeling of Data Warehouses
Modeling data warehouses: dimensions & measures
Star schema
A fact table in the middle connected to a set of dimension tables
Snowflake schema
A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake
Fact constellations
Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation

16. Example of Star Schema Sales Fact Table Measures Time time_key Item
day
day_of_the_week
month
quarter
year
Sales Fact Table
Item
item_key
item_name
brand
type
supplier_type
Time_key
Item_key
Branch_key
Location
Branch
Location_key
location_key
street
city
province_or_street
country
branch_key
branch_name
branch_type
Unit_sold
Euros_sold
Avg_sales
Measures

17. Example of Snowflake Schema
Supplier
Time
supplier_key
supplier_type
time_key
day
day_of_the_week
month
quarter
year
Item
Sales Fact Table
item_key
item_name
brand
type
supplier_key
Avg_sales
Euros_sold
Unit_sold
Location_key
Branch_key
Item_key
Time_key
city_key
city
province_or_street
country
City
Branch
branch_key
branch_name
branch_type
location_key
street
city_key
Location
Measures

18. Example of Fact Constellation
Shipping Fact Table
Time
unit_shipped
Euros_sold
to_location
from_location
shipper_key
Item_key
Time_key
time_key
day
day_of_the_week
month
quarter
year
item_key
item_name
brand
type
supplier_key
Item
Sales Fact Table
Avg_sales
Euros_sold
Unit_sold
Location_key
Branch_key
Item_key
Time_key
Branch
branch_key
branch_name
branch_type
Location
location_key
street
city
Province/street
country
shipper_key
shipper_name
location_key
shipper_type
shipper
Measures

19. DMQL: Language Primitives
Cube Definition (Fact Table)
define cube <cube_name> [<dimension_list>]: <measure_list>
Dimension Definition (Dimension Table)
define dimension <dimension_name> as (<attribute_or_subdimension_list>)
Special Case (Shared Dimension Tables)
First time as “cube definition”
define dimension <dimension_name> as <dimension_name_first_time> in cube <cube_name_first_time>

20. Defining a Star Schema in DMQL
define cube sales_star [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars),
avg_sales = avg(sales_in_dollars),
units_sold = count(*)
define dimension time as
(time_key, day, day_of_week, month, quarter, year)
define dimension item as
(item_key, item_name, brand, type, supplier_type)
define dimension branch as
(branch_key, branch_name, branch_type)
define dimension location as
(location_key, street, city, province_or_state, country)

21. Defining a Snowflake Schema in DMQL
define cube sales_snowflake [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars),
avg_sales = avg(sales_in_dollars),
units_sold = count(*)
define dimension time as
(time_key, day, day_of_week, month, quarter, year)
define dimension item as
(item_key, item_name, brand, type, supplier(supplier_key, supplier_type))
define dimension branch as
(branch_key, branch_name, branch_type)
define dimension location as
(location_key, street, city(city_key, province_or_state, country))

22. Defining a Fact Constellation in DMQL
define cube sales [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week, month, quarter, year)
define dimension item as (item_key, item_name, brand, type, supplier_type)
define dimension branch as (branch_key, branch_name, branch_type)
define dimension location as (location_key, street, city, province_or_state, country)
define cube shipping [time, item, shipper, from_location, to_location]:
dollar_cost = sum(cost_in_dollars), unit_shipped = count(*)
define dimension time as time in cube sales
define dimension item as item in cube sales
define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type)
define dimension from_location as location in cube sales
define dimension to_location as location in cube sales

23. Measures: Three Categories
Distributive
if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning.
E.g., count(), sum(), min(), max()
Algebraic
if it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function.
E.g., avg(), min_N(), standard_deviation()
Holistic
if there is no constant bound on the storage size needed to describe a subaggregate.
E.g., median(), mode(), rank()

24. A Concept Hierarchy: Dimension (location)
all
all
North_America
...
Europe
region
Canada
...
Mexico
Ireland
...
France
country
Dublin
...
city
Toronto
...
Belfast
Belfield
...
Blackrock
office

25. View of Warehouses and Hierarchies
Specification of hierarchies
Schema hierarchy
day < {month < quarter; week} < year
Set_grouping hierarchy
{1..10} < inexpensive

26. Multidimensional Data
Sales volume as a function of product, month, and region
Dimensions: Product, Location, Time
Hierarchical summarization paths
Region
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
Product
Month

27. A Sample Data Cube All, All, All Date Product Country
Total annual sales
of TV in Ireland
Date
Product
Country
All, All, All
sum TV
VCR PC
1Qtr
2Qtr
3Qtr
4Qtr
Ireland
France
Germany

28. Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
country
product
date
1-D cuboids
product,date
product,country
date, country
2-D cuboids
3-D(base) cuboid
product, date, country

29. Browsing a Data Cube Visualization OLAP capabilities
Interactive manipulation

30. Typical OLAP Operations
Roll up (drill-up): summarize data
by climbing up hierarchy or by dimension reduction
Drill down (roll down): reverse of roll-up
from higher level summary to lower level summary or detailed data, or introducing new dimensions
Slice and dice
project and select
Pivot (rotate)
reorient the cube, visualization, 3D to series of 2D planes.
Other operations
drill across: involving (across) more than one fact table
drill through: through the bottom level of the cube to its back-end relational tables (using SQL)

31. A Star-Net Query Model Each circle is called a footprint
Customer Orders
Shipping Method
Customer
CONTRACTS
AIR-EXPRESS
ORDER
TRUCK
PRODUCT LINE
Time
Product
ANNUALY
QTRLY
DAILY
PRODUCT ITEM
PRODUCT GROUP
CITY
SALES PERSON
COUNTRY
DISTRICT
REGION
DIVISION
Each circle is called a footprint
Location
Promotion
Organization

32. Outline What is a data warehouse? A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
Further development of data cube technology
From data warehousing to data mining

33. Design of a Data Warehouse: A Business Analysis Framework
Four views regarding the design of a data warehouse
Top-down view
allows selection of the relevant information necessary for the data warehouse
Data source view
exposes the information being captured, stored, and managed by operational systems
Data warehouse view
consists of fact tables and dimension tables
Business query view
sees the perspectives of data in the warehouse from the view of end-user

34. Data Warehouse Design Process
Top-down, bottom-up approaches or a combination of both
Top-down: Starts with overall design and planning (mature)
Bottom-up: Starts with experiments and prototypes (rapid)
From software engineering point of view
Waterfall: structured and systematic analysis at each step before proceeding to the next
Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around
Typical data warehouse design process
Choose a business process to model, e.g., orders, invoices, etc.
Choose the grain (atomic level of data) of the business process
Choose the dimensions that will apply to each fact table record
Choose the measure that will populate each fact table record

35. Multi-Tiered Architecture
Operational
DBs
other
sources
Monitor
&
Integrator
OLAP Server
Metadata
Extract
Transform
Load
Refresh
Analysis
Query
Reports
Data mining
Serve
Data
Warehouse
Data Marts
Data Sources
Data Storage
OLAP Engine
Front-End Tools

36. Three Data Warehouse Models
Enterprise warehouse
collects all of the information about subjects spanning the entire organization
Data Mart
a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart
Independent vs. dependent (directly from warehouse) data mart
Virtual warehouse
A set of views over operational databases
Only some of the possible summary views may be materialized

37. Data Warehouse Development: A Recommended Approach
Multi-Tier Data Warehouse
Distributed Data Marts
Enterprise Data Warehouse
Data Mart
Data Mart
Model refinement
Model refinement
Define a high-level corporate data model

38. OLAP Server Architectures
Relational OLAP (ROLAP)
Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces
Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services
greater scalability
Multidimensional OLAP (MOLAP)
Array-based multidimensional storage engine (sparse matrix techniques)
fast indexing to pre-computed summarized data
Hybrid OLAP (HOLAP)
User flexibility, e.g., low level: relational, high-level: array
Specialized SQL servers
specialized support for SQL queries over star/snowflake schemas